Applied Artificial Intelligence Senior Scientist

Opening Date: November 21, 2025Closing Date: December 5, 2025Ref:IFAE2025/50_PIC_SO_INVESTIGADOR About IFAE The Institut de Física d'Altes Energies (IFAE) is a public consortium of the Generalitat de Catalunya, the Autonomous Government of Catalonia, and the Universitat Autònoma de Barcelona (UAB, a public university) whose main objective is to carry out research and to contribute to the development of high energy physics from a theoretical, experimental and technological point of view. IFAE has the status of a "University Institute" attached to the UAB and its premises are within the campus of the University. About PIC Port d'Informació Científica (PIC) is a scientific-technological center operated through a collaboration agreement between IFAE and CIEMAT specialized in developing advanced tools and methods for scientific data analysis. We participate in multidisciplinary research projects with challenging data environments, including particle physics, astrophysics, materials science, and bioimaging, all characterized by complex and data-intensive analyses. Our mission is to accelerate scientific discovery by combining Artificial Intelligence (AI), High-Throughput Computing (HTC), and Big Data technologies within collaborative and FAIR data research ecosystems. PIC's Applied AI group develops and deploys advanced AI methodologies to transform how scientific data are analyzed across domains. We are seeking a Senior Scientist in Applied Artificial Intelligence to consolidate and expand this activity, leading innovative research and development in data-driven science and AI-based data analysis. Required skills and evaluation criteria Competencies We expect from you: Strong ability to understand complex experimental and data environments and design AI solutions addressing specific scientific challenges. Demonstrated experience in deep learning, representation learning, and AI-based image/signal restoration. Experience developing and training deep learning models on large-scale datasets and multi-GPU or distributed computing environments. Proactive, independent, and capable of leading multidisciplinary projects. Excellent communication and collaboration skills. Experience supervising postdoctoral researchers or students in a scientific or technical setting. Proven track record of scientific publications and project coordination. Ability to define and communicate a long-term vision for AI applications in scientific data analysis. Requirements PhD in Science (Physics, Computer Science, Mathematics, Materials Science, or related). Minimum of ten (10) years of scientific programming experience. Experience with modern AI frameworks (e.g. PyTorch, Jax). Strong programming experience in Python, including the design of AI-driven data processing and analysis pipelines using scientific and data engineering libraries (e.g., Numpy, Pandas, PyTorch, JAX, Scikit-learn). Excellent command of written and spoken English. The process of evaluation and selection guarantees the principles of open competition, publicity and transparency, according to the following criteria: Research track record of scientific publications and project coordination, 25 Experience developing deep learning models on large datasets and distributed computing environments, 25 Experience handling complex experimental and data environments in different disciplines, 25 Experience supervising postdoctoral researchers or students, 25 What will be your role? You will work as a lead researcher within the Applied AI Group at PIC. Your job main responsibilities will include: Lead the design and implementation of AI/ML models for scientific data analysis and image restoration across multiple research domains. Collaborate with scientists in astronomy, particle physics, materials science, quantum technologies and bioimaging to identify requirements and develop domain-adapted yet generalizable AI workflows. Work with PIC's computing and data engineering teams to ensure efficient integration of AI applications in distributed computing environments. Supervise and mentor junior researchers and PhD students within the Applied AI Group. Publish scientific results in peer-reviewed journals and contribute to open-source software and FAIR data initiatives. Identify and help secure new projects and funding opportunities in the field of applied AI for scientific research. Teach and coordinate the PIC Deep Learning course. In alignment with the IFAE's Severo Ochoa strategic program, the position will also contribute to: Strategic support for IFAE's Severo Ochoa pillars, developing and applying AI methodologies for the Einstein telescope and Medical Physics programs, ensuring scalable data analysis, uncertainty quantification, and real-time decision frameworks. Integration of AI across IFAE domains, fostering collaboration with groups in neutrino physics, quantum technologies, cosmology, and materials science to